Abstract

The phenomenon of intentional binding pertains to the perceived connection between a voluntary action and its anticipated result. When an individual intends an outcome, it appears to subjectively extend in time due to a pre-activation of the intended result, particularly evident at shorter action-outcome delays. However, there is a concern that the operationalization of intention might have led to a mixed interpretation of the outcome expansion attributed to the pre-activation of intention, given the sensitivity of time perception and intentional binding to external cues that could accelerate the realization of expectations. To investigate the expansion dynamics of an intended outcome, we employed a modified version of the temporal bisection task in two experiments. Experiment 1 considered the action-outcome delay as a within-subject factor, while experiment 2 treated it as a between-subject factor. The results revealed that the temporal expansion of an intended outcome was only evident under the longer action-outcome delay condition. We attribute this observation to working memory demands and attentional allocation due to temporal relevancy, and not due to pre-activation. The discrepancy in effects across studies is explained by operationalizing different components of the intentional binding effect, guided by the cue integration theory. Moreover, we discussed speculative ideas regarding the involvement of specific intentions based on the proximal intent distal intent (PIDI) theory, and whether causality plays a role in temporal binding. Our study contributes to the understanding of how intention influences time perception and sheds light on how various methodological factors, cues, and delays can impact the dynamics of temporal expansion associated with an intended outcome.

Abstract

The intentional binding (IB) phenomenon reflects a perceived attraction between a voluntary action and an intendedconsequence. Recent research has shown that during IB, intended outcomes expand in time. Although, this effect was significant only for shorter action-outcome delays. However, literature on IB suggests it can also exist for long delays. To address this, we implemented a temporal reproduction task to observe the expansion of an intended outcome. Results revealed the expansion of an intended outcome in the shorter and longer action-outcome delays. These results were discussed under the PIDI (proximal intent distal intent) framework since, using this methodology, both types of intention can be operationalised. Proximal for shorter delays under the motor action, and distal for longer delays as the inference made towards the objective duration to be reproduced. Our implementation allows a robust way to observe the temporal dynamics of an intended outcome, irrespective of the action-outcome delay.

Abstract

Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although previous studies have proposed effective solutions for English, no work exists for Indian languages. First, we contribute two large datasets, mWIKIBIAS and mWNC, covering 8 languages, for the bias detection and mitigation tasks respectively. Next, we investigate the effectiveness of popular multilingual Transformer-based models for the two tasks by modeling detection as a binary classification problem and mitigation as a style transfer problem. We make the code and data publicly available.

Abstract

Subjective bias in Wikipedia textual data is a significant problem and affects millions of readers worldwide. Though some monolingual work has been done in classifying and debiasing biased text in resource-rich languages, the low-resource languages with large numbers of speakers remain unattended. We present an approach for the dual problems of multilingual bias detection and its mitigation with a thorough analysis. In this work, we establish competitive baselines on our preliminary approach, which includes classification-based modelling for bias detection on a multilingual dataset curated from existing monolingual sources. For the problem of bias mitigation, we follow the style transfer paradigm and model using transformer-based seq2seq architectures. We also discuss several approaches for further improvement in both problems as a part of our ongoing work.

Abstract

The paper presents a novel approach for automated cross-lingual fact-checking that extracts and verifies information from Wikipedia using references. The problem involves determining whether a factoid in an article is supported or needs additional citations based on the provided references, with granularity at the fact level. We introduce a cross-lingual manually annotated dataset for fact extraction and verification and an entirely automated pipeline for the task. The proposed solution operates entirely in a cross-lingual setting, where the article text and the references can be in any language. The pipeline integrates several natural language processing techniques to extract the relevant facts from the input sources. The extracted facts are then verified against the references, leveraging the semantic relationships between the facts and the reference sources. Experimental evaluation on a large-scale dataset demonstrates the effectiveness and efficiency of the proposed approach in handling cross-lingual fact-checking tasks. We make our code and data publicly available.

Abstract

Deep Reinforcement Learning is quickly becoming a popular method for training autonomous Unmanned Aerial Vehicles (UAVs). Our work analyzes the effects of measurement uncertainty on the performance of Deep Reinforcement Learning (DRL) based waypoint navigation and obstacle avoidance for UAVs. Measurement uncertainty originates from noise in the sensors used for localization and detecting obstacles. Measurement uncertainty/noise is considered to follow a Gaussian probability distribution with unknown non-zero mean and variance. We evaluate the performance of a DRL agent, trained using the Proximal Policy Optimization (PPO) algorithm in an environment with continuous state and action spaces. The environment is randomized with different numbers of obstacles for each simulation episode in the presence of varying degrees of noise, to capture the effects of realistic sensor measurements. Denoising techniques like the low pass filter and Kalman filter improve performance in the presence of unbiased noise. Moreover, we show that artificially injecting noise into the measurements during evaluation actually improves performance in certain scenarios. Extensive training and testing of the DRL agent under various UAV navigation scenarios are performed in the PyBullet physics simulator. To evaluate the practical validity of our method, we port the policy trained in simulation onto a real UAV without any further modifications and verify the results in a real-world environment.

Abstract

Inthisstudy,weinvestigatetheprimingeffectofcamera-inducedobjectifying-gaze,operationalisedviasexualisedmusic video(MV),onuniversitystudentswhenlookingattargetfemaleimagesintraditional(saree,salwar)andwestern(shirtpant, short-dress)attires intheIndiandemographics. Weshowedparticipantstwovideoswherefemaleleadswore unrevealingIndianattire(saree)andrevealingwesternclothinginsexualisedandtraditionalMVsrespectively. We quantifiedgazeasfixation-durationandvisit-count(revisitfrequency)metrics.Ourfindingsreportthatthereisapriming effectofsexualisedmedia.Femaleimagesinallattiresareequallyobjectifiedandbody-biasedgazeismorepronounced insexualisedMVpriming,short-dressismoreobjectifiedintraditionalMVpriming,andsareeislessobjectifiedoverall, whichconfirmsthesocialstereotypesonattireandrespectability—traditionallyclad(vs. non-traditional)womenare seenasmoremoralandworthyofrespectandprotectioncomparedtowomeninhypersexualizedandobjectifiedroles.

Abstract

No Results Found

Abstract

To foster the development of pedagogically potent and ethically sound AI-integrated learning landscapes, it is pivotal to critically explore the perceptions and experiences of the users immersed in these contexts. In this study, we perform a thorough qualitative content analysis across four key social media platforms. Our goal is to understand the user experience (UX) and views of early adopters of ChatGPT across different educational sectors. The results of our research show that ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training. In social media dialogues, the topics most frequently associated with ChatGPT are productivity, efficiency, and ethics. Early adopters' attitudes towards ChatGPT are multifaceted. On one hand, some users view it as a transformative tool capable of amplifying student self-efficacy and learning motivation. On the other hand, there is a degree of apprehension among concerned users. They worry about a potential overdependence on the AI system, which they fear might encourage superficial learning habits and erode students’ social and critical thinking skills. This dichotomy of opinions underscores the complexity of Human-AI Interaction in educational contexts. Our investigation adds depth to this ongoing discourse, providing crowd-sourced insights for educators and learners who are considering incorporating ChatGPT or similar generative AI tools into their pedagogical strategies

Abstract

A method for detecting object in an image in a target environment that is adapted to a source environment using a machine learning model is provided. The method includes (i) extracting features from source image associated with source environment and target image associated with target environment, (ii) generating a feature map based on the features, (iii) generating a pixel-wise probability output map (iv) determining a first environment invariant feature map by combining the feature map with the pixel-wise probability output map, (v) determining a second environment invariant feature map by combining the first environment invariant feature map and the features, (vi) generating environment invariant feature maps at different instances, (vii) extracting environment invariant features based on the environment invariant feature maps, (viii) detecting the object in the image in the target environment that is adapted to the source environment by training the machine learning model using the environment invariant features.